2,643 research outputs found
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
We present a heuristic based algorithm to induce \textit{nonmonotonic} logic
programs that will explain the behavior of XGBoost trained classifiers. We use
the technique based on the LIME approach to locally select the most important
features contributing to the classification decision. Then, in order to explain
the model's global behavior, we propose the LIME-FOLD algorithm ---a
heuristic-based inductive logic programming (ILP) algorithm capable of learning
non-monotonic logic programs---that we apply to a transformed dataset produced
by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm.
Our experiments with UCI standard benchmarks suggest a significant improvement
in terms of classification evaluation metrics. Meanwhile, the number of induced
rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
Learning Rules from User Behaviour
Accepted versio
Inductive logic programming at 30: a new introduction
Inductive logic programming (ILP) is a form of machine learning. The goal of
ILP is to induce a hypothesis (a set of logical rules) that generalises
training examples. As ILP turns 30, we provide a new introduction to the field.
We introduce the necessary logical notation and the main learning settings;
describe the building blocks of an ILP system; compare several systems on
several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol);
highlight key application areas; and, finally, summarise current limitations
and directions for future research.Comment: Paper under revie
Inductive learning of answer set programs for autonomous surgical task planning
The quality of robot-assisted surgery can be improved and the use of hospital resources can be optimized by enhancing autonomy and reliability in the robot’s operation. Logic programming is a good choice for task planning in robot-assisted surgery because it supports reliable reasoning with domain knowledge and increases transparency in the decision making. However, prior knowledge of the task and the domain is typically incomplete, and it often needs to be refined from executions of the surgical task(s) under consideration to avoid sub-optimal performance. In this paper, we investigate the applicability of inductive logic programming for learning previously unknown axioms governing domain dynamics. We do so under answer set semantics for a benchmark surgical training task, the ring transfer. We extend our previous work on learning the immediate preconditions of actions and constraints, to also learn axioms encoding arbitrary temporal delays between atoms that are effects of actions under the event calculus formalism. We propose a systematic approach for learning the specifications of a generic robotic task under the answer set semantics, allowing easy knowledge refinement with iterative learning. In the context of 1000 simulated scenarios, we demonstrate the significant improvement in performance obtained with the learned axioms compared with the hand-written ones; specifically, the learned axioms address some critical issues related to the plan computation time, which is promising for reliable real-time performance during surgery
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